An energy-aware framework for cascaded detection algorithms

energy-aware;signal detection;incremental refinement;passive vigilance;scalable systems

Jun, David M. ; Jones ; Douglas L.

关键词: energy-aware;    signal detection;    incremental refinement;    passive vigilance;    scalable systems;   

Others  :  https://www.ideals.illinois.edu/bitstream/handle/2142/18395/Jun_David.pdf?sequence=1&isAllowed=y

美国|英语

来源: The Illinois Digital Environment for Access to Learning and Scholarship

【 摘 要 】

Low-power, scalable detection systems require aggressive techniques to achieve energy efficiency. Algorithmic methods that can reduce energy consumption by compromising performance are known as being energy-aware.The cascade architecture is known for being energy-efficient, but without proper operation can end up being energy-inefficient in practice. In this thesis, we propose a framework that imposes energy-awareness on cascaded detection algorithms, which enforces proper operation of the cascade to maximize detection performance for a given energy budget. This is achieved by solving our proposed energy-constrained version of the Neyman-Pearson detection criterion, resulting in detector thresholds that can be updated to dynamically adjust to time-varying system resources and requirements.Sufficient conditions for a global solution for a cascade of an arbitrary number of detectors are given. Explicit solutions are derived for a two-stage cascade. Applied to a canonical detection problem, simulations show that our energy-aware cascaded detectors outperform an energy-aware detection algorithm based on incremental refinement, an existing alternate approach to developing energy-aware algorithms. Combining our framework with incremental refinement reveals a promising approach to developing energy-aware energy-efficient detection systems.